Multi-objective optimization of temperature uniformity in the immersion liquid cooling cabinet with Taguchi-based grey relational analysis

• Published in: International communications in heat and mass transfer Vol. 154; p. 107395
• Main Authors: Liu, Hong-min, Chang, Zhe
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2024
• Subjects: Grey relational analysis; Single-phase immersion liquid cooling; Taguchi method; Uniformity evaluation; Grey relational analysis; Single-phase immersion liquid cooling; Uniformity evaluation; Taguchi method
• ISSN: 0735-1933
• DOI: 10.1016/j.icheatmasstransfer.2024.107395

Single-phase immersion liquid cooling technology can meet the cooling needs of high-density Data Centers. To improve the temperature uniformity in an immersion liquid cooling cabinet and prevent local hot spots that reduce the efficiency of servers, the structural parameters of the cabinet were optimized in this study. The fluid path is mainly affected by the position of inlet and outlet, length, Angle and number of baffles. To evaluate the effects of these factors on the temperature uniformity in the cabinet, Taguchi-based grey relational analysis method and computational fluid dynamics were used to optimize the system, the standard deviation and maximum temperature difference are determined as evaluation indices, and L16 (44) orthogonal array was used for experimental design. The baffle Angle is judged to be the most important structural parameter by the Main Effect Analysis, whose contribution rate is 49.23%. The multi-objective optimization is transformed into a single objective by the grey relational analysis, and the influence rate of each structural parameter is shown. The optimal combination of structural parameters is determined. The results show that the temperature uniformity of the optimized cabinet is improved by 46%, which provides a reference for the design of immersion liquid cooling Data Centers. •The interaction between various factors is taken into account.•Good agreement between the experimental results and those by using Fluent.•The influence rate and its order of each structural parameter are demonstrated.•The optimal combination of structural parameters is determined, which improves cabinet temperature uniformity by 46%.